A PyTorch implementation of GraphSAGE

This package contains a PyTorch implementation of GraphSAGE.

Authors of this code package: Tianwen Jiang ([email protected]), Tong Zhao ([email protected]).

Environment settings

• python==3.6.8
• pytorch==1.0.0

Basic Usage

Main Parameters:

--dataSet     The input graph dataset. (default: cora)
--agg_func    The aggregate function. (default: Mean aggregater)
--epochs      Number of epochs. (default: 200)
--b_sz        Batch size. (default: 20)
--seed        Random seed. (default: 824)
--num_neg     Number of negative samples in each batch. (default: 100)
--config      Config file. (default: ./src/experiments.conf)
--cuda        Use cuda if declared.

Loss function The user must specify a loss function by --learn_method, ...

Example Usage To run the unsupervised model on Cuda:

python -m src.main --epochs 100 --cuda --learn_method unsup